Great coverage of the topic, that is often omitted in Machine Learning intro books, or books on ML infrastructure.
Picked up tons of programmatic tricks on how to deal with numerical and categorical data. There are good examples in Python.

Several spots in the book are in need of more attention or re-work, imho.
The code printouts were quite long, and were hard to follow on Kindle or iPad.

Also, the appendix section on linear modeling and linear algebra doesn’t seem to belong. And it can be part of the Feature Engineering topic, but in a more intuitive and down to earth way – the coverage seemed a bit more abstract that it could benefit the reader.

Attempted to run MXNet 1.1 GPU version on Windows 10. It works well, but the process of installation has a few details that is helpful to know.
1. You can create Anaconda environment based on Python 3.6 and use environment’s pip to install GPU version of Apache MXNet on Windows – pip install mxnet-cu90 at the moment. I believe cu91 is in the works and soon will be available.
2. You still need to download and install CUDA 9.0 as well as CuDNN 7.1 (FOR WINDOWS 10, NOT WINDOWS 7 – very easy to mistake them!) from the nVidia website. If CUDA itself is available for the download without an account – to get CuDNN library you need to have a free account on the nVidia page.
3. You still need to install latest graphics card drivers on the system, even after you installed all the CUDA stuff. For me MXNet was breaking on initialization of GPU context until I’ve rolled GPU drivers.

With the current boom in the concurrency and block-chain world, I’ve decided I want to learn more about the open source crypto projects, how popular they are and how often these projects are updated. After going to github and bitbucket and manually searching for projects I’ve came to a decision that I need to build a portal that would track all of the interest points for me automatically. And as it is a portal, it might be useful for you as well.
So in a few weekends and united work of 3 coders – https://www.coingitstats.com came to life.

The project is still in very early phase – but it does provide hourly updates and a very sleek UI – thanks to my wife:-)
We have a few items on the road-map, such as adding market capitalization, introducing machine learning based on the git and market data and a bit friendlier mobile experience.